Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory356.1 KiB
Average record size in memory248.1 B

Variable types

Numeric14
Categorical17

Alerts

% aumento sal is highly overall correlated with Avaliação desempHigh correlation
Anos cargo atual is highly overall correlated with Anos empresa and 2 other fieldsHigh correlation
Anos empresa is highly overall correlated with Anos cargo atual and 3 other fieldsHigh correlation
Anos gerente atual is highly overall correlated with Anos cargo atual and 1 other fieldsHigh correlation
Anos traba is highly overall correlated with Anos empresa and 3 other fieldsHigh correlation
Anos ult prom is highly overall correlated with Anos cargo atual and 1 other fieldsHigh correlation
Avaliação desemp is highly overall correlated with % aumento salHigh correlation
Cargo is highly overall correlated with Departamento and 1 other fieldsHigh correlation
Departamento is highly overall correlated with Cargo and 1 other fieldsHigh correlation
Estado civil is highly overall correlated with Opc açõesHigh correlation
Idade is highly overall correlated with Anos trabaHigh correlation
Nível cargo is highly overall correlated with Anos traba and 2 other fieldsHigh correlation
Opc ações is highly overall correlated with Estado civilHigh correlation
Renda mensal is highly overall correlated with Anos traba and 1 other fieldsHigh correlation
Área form is highly overall correlated with DepartamentoHigh correlation
Nº empresas trab has 197 (13.4%) zeros Zeros
Treinam ultm ano has 54 (3.7%) zeros Zeros
Anos empresa has 44 (3.0%) zeros Zeros
Anos cargo atual has 244 (16.6%) zeros Zeros
Anos ult prom has 581 (39.5%) zeros Zeros
Anos gerente atual has 263 (17.9%) zeros Zeros

Reproduction

Analysis started2025-04-15 22:01:06.713043
Analysis finished2025-04-15 22:02:19.873030
Duration1 minute and 13.16 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Idade
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92381
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-04-15T19:02:20.151750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1353735
Coefficient of variation (CV)0.24741146
Kurtosis-0.40414514
Mean36.92381
Median Absolute Deviation (MAD)6
Skewness0.4132863
Sum54278
Variance83.455049
MonotonicityNot monotonic
2025-04-15T19:02:20.564360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
35 78
 
5.3%
34 77
 
5.2%
36 69
 
4.7%
31 69
 
4.7%
29 68
 
4.6%
32 61
 
4.1%
30 60
 
4.1%
33 58
 
3.9%
38 58
 
3.9%
40 57
 
3.9%
Other values (33) 815
55.4%
ValueCountFrequency (%)
18 8
 
0.5%
19 9
 
0.6%
20 11
 
0.7%
21 13
 
0.9%
22 16
 
1.1%
23 14
 
1.0%
24 26
1.8%
25 26
1.8%
26 39
2.7%
27 48
3.3%
ValueCountFrequency (%)
60 5
 
0.3%
59 10
0.7%
58 14
1.0%
57 4
 
0.3%
56 14
1.0%
55 22
1.5%
54 18
1.2%
53 19
1.3%
52 18
1.2%
51 19
1.3%

Attrition
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Não
1233 
Sim
237 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSim
2nd rowNão
3rd rowSim
4th rowNão
5th rowNão

Common Values

ValueCountFrequency (%)
Não 1233
83.9%
Sim 237
 
16.1%

Length

2025-04-15T19:02:20.964036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:21.334146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
não 1233
83.9%
sim 237
 
16.1%

Most occurring characters

ValueCountFrequency (%)
N 1233
28.0%
ã 1233
28.0%
o 1233
28.0%
S 237
 
5.4%
i 237
 
5.4%
m 237
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1233
28.0%
ã 1233
28.0%
o 1233
28.0%
S 237
 
5.4%
i 237
 
5.4%
m 237
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1233
28.0%
ã 1233
28.0%
o 1233
28.0%
S 237
 
5.4%
i 237
 
5.4%
m 237
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1233
28.0%
ã 1233
28.0%
o 1233
28.0%
S 237
 
5.4%
i 237
 
5.4%
m 237
 
5.4%

Viagens trab
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Raramente
1043 
Frequentemente
277 
Não viaja
150 

Length

Max length14
Median length9
Mean length9.9421769
Min length9

Characters and Unicode

Total characters14615
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRaramente
2nd rowFrequentemente
3rd rowRaramente
4th rowFrequentemente
5th rowRaramente

Common Values

ValueCountFrequency (%)
Raramente 1043
71.0%
Frequentemente 277
 
18.8%
Não viaja 150
 
10.2%

Length

2025-04-15T19:02:21.684422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:22.026543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
raramente 1043
64.4%
frequentemente 277
 
17.1%
não 150
 
9.3%
viaja 150
 
9.3%

Most occurring characters

ValueCountFrequency (%)
e 3471
23.7%
a 2386
16.3%
n 1597
10.9%
t 1597
10.9%
r 1320
 
9.0%
m 1320
 
9.0%
R 1043
 
7.1%
u 277
 
1.9%
q 277
 
1.9%
F 277
 
1.9%
Other values (7) 1050
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3471
23.7%
a 2386
16.3%
n 1597
10.9%
t 1597
10.9%
r 1320
 
9.0%
m 1320
 
9.0%
R 1043
 
7.1%
u 277
 
1.9%
q 277
 
1.9%
F 277
 
1.9%
Other values (7) 1050
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3471
23.7%
a 2386
16.3%
n 1597
10.9%
t 1597
10.9%
r 1320
 
9.0%
m 1320
 
9.0%
R 1043
 
7.1%
u 277
 
1.9%
q 277
 
1.9%
F 277
 
1.9%
Other values (7) 1050
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3471
23.7%
a 2386
16.3%
n 1597
10.9%
t 1597
10.9%
r 1320
 
9.0%
m 1320
 
9.0%
R 1043
 
7.1%
u 277
 
1.9%
q 277
 
1.9%
F 277
 
1.9%
Other values (7) 1050
 
7.2%

Tarifa diária
Real number (ℝ)

Distinct886
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.48571
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-04-15T19:02:22.413699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile165.35
Q1465
median802
Q31157
95-th percentile1424.1
Maximum1499
Range1397
Interquartile range (IQR)692

Descriptive statistics

Standard deviation403.5091
Coefficient of variation (CV)0.50282403
Kurtosis-1.2038228
Mean802.48571
Median Absolute Deviation (MAD)344
Skewness-0.0035185684
Sum1179654
Variance162819.59
MonotonicityNot monotonic
2025-04-15T19:02:22.890794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
691 6
 
0.4%
408 5
 
0.3%
530 5
 
0.3%
1329 5
 
0.3%
1082 5
 
0.3%
329 5
 
0.3%
829 4
 
0.3%
1469 4
 
0.3%
267 4
 
0.3%
217 4
 
0.3%
Other values (876) 1423
96.8%
ValueCountFrequency (%)
102 1
 
0.1%
103 1
 
0.1%
104 1
 
0.1%
105 1
 
0.1%
106 1
 
0.1%
107 1
 
0.1%
109 1
 
0.1%
111 3
0.2%
115 1
 
0.1%
116 2
0.1%
ValueCountFrequency (%)
1499 1
 
0.1%
1498 1
 
0.1%
1496 2
0.1%
1495 3
0.2%
1492 1
 
0.1%
1490 4
0.3%
1488 1
 
0.1%
1485 3
0.2%
1482 1
 
0.1%
1480 2
0.1%

Departamento
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Pesquisa e Desenvolvimento
961 
Vendas
446 
Recursos Humanos
 
63

Length

Max length26
Median length26
Mean length19.503401
Min length6

Characters and Unicode

Total characters28670
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVendas
2nd rowPesquisa e Desenvolvimento
3rd rowPesquisa e Desenvolvimento
4th rowPesquisa e Desenvolvimento
5th rowPesquisa e Desenvolvimento

Common Values

ValueCountFrequency (%)
Pesquisa e Desenvolvimento 961
65.4%
Vendas 446
30.3%
Recursos Humanos 63
 
4.3%

Length

2025-04-15T19:02:23.387590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:24.080800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
pesquisa 961
27.8%
e 961
27.8%
desenvolvimento 961
27.8%
vendas 446
12.9%
recursos 63
 
1.8%
humanos 63
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 5314
18.5%
s 3518
12.3%
n 2431
8.5%
o 2048
 
7.1%
1985
 
6.9%
v 1922
 
6.7%
i 1922
 
6.7%
a 1470
 
5.1%
u 1087
 
3.8%
m 1024
 
3.6%
Other values (11) 5949
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28670
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5314
18.5%
s 3518
12.3%
n 2431
8.5%
o 2048
 
7.1%
1985
 
6.9%
v 1922
 
6.7%
i 1922
 
6.7%
a 1470
 
5.1%
u 1087
 
3.8%
m 1024
 
3.6%
Other values (11) 5949
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28670
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5314
18.5%
s 3518
12.3%
n 2431
8.5%
o 2048
 
7.1%
1985
 
6.9%
v 1922
 
6.7%
i 1922
 
6.7%
a 1470
 
5.1%
u 1087
 
3.8%
m 1024
 
3.6%
Other values (11) 5949
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28670
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5314
18.5%
s 3518
12.3%
n 2431
8.5%
o 2048
 
7.1%
1985
 
6.9%
v 1922
 
6.7%
i 1922
 
6.7%
a 1470
 
5.1%
u 1087
 
3.8%
m 1024
 
3.6%
Other values (11) 5949
20.7%

Distância casa
Real number (ℝ)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.793885
Minimum1.60934
Maximum46.67086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-04-15T19:02:24.474208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.60934
5-th percentile1.60934
Q13.21868
median11.26538
Q322.53076
95-th percentile41.84284
Maximum46.67086
Range45.06152
Interquartile range (IQR)19.31208

Descriptive statistics

Standard deviation13.046701
Coefficient of variation (CV)0.88189823
Kurtosis-0.2248334
Mean14.793885
Median Absolute Deviation (MAD)8.0467
Skewness0.958118
Sum21747.011
Variance170.21641
MonotonicityNot monotonic
2025-04-15T19:02:24.867286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3.21868 211
14.4%
1.60934 208
14.1%
16.0934 86
 
5.9%
14.48406 85
 
5.8%
4.82802 84
 
5.7%
11.26538 84
 
5.7%
12.87472 80
 
5.4%
8.0467 65
 
4.4%
6.43736 64
 
4.4%
9.65604 59
 
4.0%
Other values (19) 444
30.2%
ValueCountFrequency (%)
1.60934 208
14.1%
3.21868 211
14.4%
4.82802 84
 
5.7%
6.43736 64
 
4.4%
8.0467 65
 
4.4%
9.65604 59
 
4.0%
11.26538 84
 
5.7%
12.87472 80
 
5.4%
14.48406 85
5.8%
16.0934 86
5.9%
ValueCountFrequency (%)
46.67086 27
1.8%
45.06152 23
1.6%
43.45218 12
0.8%
41.84284 25
1.7%
40.2335 25
1.7%
38.62416 28
1.9%
37.01482 27
1.8%
35.40548 19
1.3%
33.79614 18
1.2%
32.1868 25
1.7%

Formação acad
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
572 
4
398 
2
282 
1
170 
5
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Length

2025-04-15T19:02:25.341940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:25.762603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Área form
Categorical

High correlation 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Ciências biológicas
606 
Ciências médicas
464 
Marketing
159 
Formação técnica
132 
Outros
82 

Length

Max length19
Median length16
Mean length15.921769
Min length6

Characters and Unicode

Total characters23405
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCiências biológicas
2nd rowCiências biológicas
3rd rowOutros
4th rowCiências biológicas
5th rowCiências médicas

Common Values

ValueCountFrequency (%)
Ciências biológicas 606
41.2%
Ciências médicas 464
31.6%
Marketing 159
 
10.8%
Formação técnica 132
 
9.0%
Outros 82
 
5.6%
Recursos Humanos 27
 
1.8%

Length

2025-04-15T19:02:26.211957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:26.637619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ciências 1070
39.6%
biológicas 606
22.5%
médicas 464
17.2%
marketing 159
 
5.9%
formação 132
 
4.9%
técnica 132
 
4.9%
outros 82
 
3.0%
recursos 27
 
1.0%
humanos 27
 
1.0%

Most occurring characters

ValueCountFrequency (%)
i 4107
17.5%
a 2590
11.1%
c 2431
10.4%
s 2303
9.8%
n 1388
 
5.9%
1229
 
5.3%
C 1070
 
4.6%
ê 1070
 
4.6%
o 1006
 
4.3%
g 765
 
3.3%
Other values (18) 5446
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23405
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 4107
17.5%
a 2590
11.1%
c 2431
10.4%
s 2303
9.8%
n 1388
 
5.9%
1229
 
5.3%
C 1070
 
4.6%
ê 1070
 
4.6%
o 1006
 
4.3%
g 765
 
3.3%
Other values (18) 5446
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23405
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 4107
17.5%
a 2590
11.1%
c 2431
10.4%
s 2303
9.8%
n 1388
 
5.9%
1229
 
5.3%
C 1070
 
4.6%
ê 1070
 
4.6%
o 1006
 
4.3%
g 765
 
3.3%
Other values (18) 5446
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23405
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 4107
17.5%
a 2590
11.1%
c 2431
10.4%
s 2303
9.8%
n 1388
 
5.9%
1229
 
5.3%
C 1070
 
4.6%
ê 1070
 
4.6%
o 1006
 
4.3%
g 765
 
3.3%
Other values (18) 5446
23.3%

Satisf amb
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
453 
4
446 
2
287 
1
284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Length

2025-04-15T19:02:27.124219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:27.492318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring characters

ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Gênero
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Masculino
882 
Feminino
588 

Length

Max length9
Median length9
Mean length8.6
Min length8

Characters and Unicode

Total characters12642
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFeminino
2nd rowMasculino
3rd rowMasculino
4th rowFeminino
5th rowMasculino

Common Values

ValueCountFrequency (%)
Masculino 882
60.0%
Feminino 588
40.0%

Length

2025-04-15T19:02:27.905218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:28.279269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
masculino 882
60.0%
feminino 588
40.0%

Most occurring characters

ValueCountFrequency (%)
i 2058
16.3%
n 2058
16.3%
o 1470
11.6%
M 882
7.0%
a 882
7.0%
s 882
7.0%
c 882
7.0%
u 882
7.0%
l 882
7.0%
F 588
 
4.7%
Other values (2) 1176
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2058
16.3%
n 2058
16.3%
o 1470
11.6%
M 882
7.0%
a 882
7.0%
s 882
7.0%
c 882
7.0%
u 882
7.0%
l 882
7.0%
F 588
 
4.7%
Other values (2) 1176
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2058
16.3%
n 2058
16.3%
o 1470
11.6%
M 882
7.0%
a 882
7.0%
s 882
7.0%
c 882
7.0%
u 882
7.0%
l 882
7.0%
F 588
 
4.7%
Other values (2) 1176
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2058
16.3%
n 2058
16.3%
o 1470
11.6%
M 882
7.0%
a 882
7.0%
s 882
7.0%
c 882
7.0%
u 882
7.0%
l 882
7.0%
F 588
 
4.7%
Other values (2) 1176
9.3%

Tarifa hora
Real number (ℝ)

Distinct71
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.891156
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-04-15T19:02:28.703486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q383.75
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation20.329428
Coefficient of variation (CV)0.30853044
Kurtosis-1.1963985
Mean65.891156
Median Absolute Deviation (MAD)18
Skewness-0.032310953
Sum96860
Variance413.28563
MonotonicityNot monotonic
2025-04-15T19:02:29.216449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 29
 
2.0%
98 28
 
1.9%
42 28
 
1.9%
48 28
 
1.9%
84 28
 
1.9%
57 27
 
1.8%
79 27
 
1.8%
96 27
 
1.8%
54 26
 
1.8%
52 26
 
1.8%
Other values (61) 1196
81.4%
ValueCountFrequency (%)
30 19
1.3%
31 15
1.0%
32 24
1.6%
33 19
1.3%
34 12
0.8%
35 18
1.2%
36 18
1.2%
37 18
1.2%
38 13
0.9%
39 17
1.2%
ValueCountFrequency (%)
100 19
1.3%
99 20
1.4%
98 28
1.9%
97 21
1.4%
96 27
1.8%
95 23
1.6%
94 22
1.5%
93 16
1.1%
92 25
1.7%
91 18
1.2%

Envolv trab
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
868 
2
375 
4
144 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Length

2025-04-15T19:02:29.713323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:30.065200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Nível cargo
Categorical

High correlation 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
543 
2
534 
3
218 
4
106 
5
69 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Length

2025-04-15T19:02:30.457412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:30.819700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Cargo
Categorical

High correlation 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Executivo de vendas
326 
Cientista pesquisador
292 
Técnico de laboratório
259 
Diretor de produção
145 
Representante de saúde
131 
Other values (4)
317 

Length

Max length23
Median length22
Mean length19.480272
Min length7

Characters and Unicode

Total characters28636
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExecutivo de vendas
2nd rowCientista pesquisador
3rd rowTécnico de laboratório
4th rowCientista pesquisador
5th rowTécnico de laboratório

Common Values

ValueCountFrequency (%)
Executivo de vendas 326
22.2%
Cientista pesquisador 292
19.9%
Técnico de laboratório 259
17.6%
Diretor de produção 145
9.9%
Representante de saúde 131
8.9%
Gerente 102
 
6.9%
Representante de vendas 83
 
5.6%
Diretor de pesquisa 80
 
5.4%
Recursos Humanos 52
 
3.5%

Length

2025-04-15T19:02:31.241764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:31.658824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
de 1024
26.5%
vendas 409
 
10.6%
executivo 326
 
8.4%
cientista 292
 
7.6%
pesquisador 292
 
7.6%
técnico 259
 
6.7%
laboratório 259
 
6.7%
diretor 225
 
5.8%
representante 214
 
5.5%
produção 145
 
3.8%
Other values (5) 417
10.8%

Most occurring characters

ValueCountFrequency (%)
e 3993
13.9%
2392
 
8.4%
i 2025
 
7.1%
o 2014
 
7.0%
d 2001
 
7.0%
a 1988
 
6.9%
s 1946
 
6.8%
t 1924
 
6.7%
r 1773
 
6.2%
n 1542
 
5.4%
Other values (21) 7038
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28636
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3993
13.9%
2392
 
8.4%
i 2025
 
7.1%
o 2014
 
7.0%
d 2001
 
7.0%
a 1988
 
6.9%
s 1946
 
6.8%
t 1924
 
6.7%
r 1773
 
6.2%
n 1542
 
5.4%
Other values (21) 7038
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28636
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3993
13.9%
2392
 
8.4%
i 2025
 
7.1%
o 2014
 
7.0%
d 2001
 
7.0%
a 1988
 
6.9%
s 1946
 
6.8%
t 1924
 
6.7%
r 1773
 
6.2%
n 1542
 
5.4%
Other values (21) 7038
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28636
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3993
13.9%
2392
 
8.4%
i 2025
 
7.1%
o 2014
 
7.0%
d 2001
 
7.0%
a 1988
 
6.9%
s 1946
 
6.8%
t 1924
 
6.7%
r 1773
 
6.2%
n 1542
 
5.4%
Other values (21) 7038
24.6%

Satisf trab
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
4
459 
3
442 
1
289 
2
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Length

2025-04-15T19:02:32.179314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:32.519274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring characters

ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Estado civil
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Casado
673 
Solteiro
470 
Divorciado
327 

Length

Max length10
Median length8
Mean length7.5292517
Min length6

Characters and Unicode

Total characters11068
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSolteiro
2nd rowCasado
3rd rowSolteiro
4th rowCasado
5th rowCasado

Common Values

ValueCountFrequency (%)
Casado 673
45.8%
Solteiro 470
32.0%
Divorciado 327
22.2%

Length

2025-04-15T19:02:32.937310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:33.293577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
casado 673
45.8%
solteiro 470
32.0%
divorciado 327
22.2%

Most occurring characters

ValueCountFrequency (%)
o 2267
20.5%
a 1673
15.1%
i 1124
10.2%
d 1000
9.0%
r 797
 
7.2%
C 673
 
6.1%
s 673
 
6.1%
S 470
 
4.2%
l 470
 
4.2%
t 470
 
4.2%
Other values (4) 1451
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2267
20.5%
a 1673
15.1%
i 1124
10.2%
d 1000
9.0%
r 797
 
7.2%
C 673
 
6.1%
s 673
 
6.1%
S 470
 
4.2%
l 470
 
4.2%
t 470
 
4.2%
Other values (4) 1451
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2267
20.5%
a 1673
15.1%
i 1124
10.2%
d 1000
9.0%
r 797
 
7.2%
C 673
 
6.1%
s 673
 
6.1%
S 470
 
4.2%
l 470
 
4.2%
t 470
 
4.2%
Other values (4) 1451
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2267
20.5%
a 1673
15.1%
i 1124
10.2%
d 1000
9.0%
r 797
 
7.2%
C 673
 
6.1%
s 673
 
6.1%
S 470
 
4.2%
l 470
 
4.2%
t 470
 
4.2%
Other values (4) 1451
13.1%

Renda mensal
Real number (ℝ)

High correlation 

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.9313
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-04-15T19:02:33.665803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.9568
Coefficient of variation (CV)0.72397455
Kurtosis1.0052327
Mean6502.9313
Median Absolute Deviation (MAD)2199
Skewness1.3698167
Sum9559309
Variance22164857
MonotonicityNot monotonic
2025-04-15T19:02:34.128966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2342 4
 
0.3%
6142 3
 
0.2%
2741 3
 
0.2%
2559 3
 
0.2%
2610 3
 
0.2%
2451 3
 
0.2%
5562 3
 
0.2%
3452 3
 
0.2%
2380 3
 
0.2%
6347 3
 
0.2%
Other values (1339) 1439
97.9%
ValueCountFrequency (%)
1009 1
0.1%
1051 1
0.1%
1052 1
0.1%
1081 1
0.1%
1091 1
0.1%
1102 1
0.1%
1118 1
0.1%
1129 1
0.1%
1200 1
0.1%
1223 1
0.1%
ValueCountFrequency (%)
19999 1
0.1%
19973 1
0.1%
19943 1
0.1%
19926 1
0.1%
19859 1
0.1%
19847 1
0.1%
19845 1
0.1%
19833 1
0.1%
19740 1
0.1%
19717 1
0.1%

Tarifa mensal
Real number (ℝ)

Distinct1427
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14313.103
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-04-15T19:02:34.577091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3384.55
Q18047
median14235.5
Q320461.5
95-th percentile25431.9
Maximum26999
Range24905
Interquartile range (IQR)12414.5

Descriptive statistics

Standard deviation7117.786
Coefficient of variation (CV)0.4972916
Kurtosis-1.2149561
Mean14313.103
Median Absolute Deviation (MAD)6206.5
Skewness0.018577808
Sum21040262
Variance50662878
MonotonicityNot monotonic
2025-04-15T19:02:34.971826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4223 3
 
0.2%
9150 3
 
0.2%
9558 2
 
0.1%
12858 2
 
0.1%
22074 2
 
0.1%
25326 2
 
0.1%
9096 2
 
0.1%
13008 2
 
0.1%
12355 2
 
0.1%
7744 2
 
0.1%
Other values (1417) 1448
98.5%
ValueCountFrequency (%)
2094 1
0.1%
2097 1
0.1%
2104 1
0.1%
2112 1
0.1%
2122 1
0.1%
2125 2
0.1%
2137 1
0.1%
2227 1
0.1%
2243 1
0.1%
2253 1
0.1%
ValueCountFrequency (%)
26999 1
0.1%
26997 1
0.1%
26968 1
0.1%
26959 1
0.1%
26956 1
0.1%
26933 1
0.1%
26914 1
0.1%
26897 1
0.1%
26894 1
0.1%
26862 1
0.1%

Nº empresas trab
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6931973
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-04-15T19:02:35.319840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009
Coefficient of variation (CV)0.92752545
Kurtosis0.010213817
Mean2.6931973
Median Absolute Deviation (MAD)1
Skewness1.0264711
Sum3959
Variance6.240049
MonotonicityNot monotonic
2025-04-15T19:02:35.701510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 521
35.4%
0 197
 
13.4%
3 159
 
10.8%
2 146
 
9.9%
4 139
 
9.5%
7 74
 
5.0%
6 70
 
4.8%
5 63
 
4.3%
9 52
 
3.5%
8 49
 
3.3%
ValueCountFrequency (%)
0 197
 
13.4%
1 521
35.4%
2 146
 
9.9%
3 159
 
10.8%
4 139
 
9.5%
5 63
 
4.3%
6 70
 
4.8%
7 74
 
5.0%
8 49
 
3.3%
9 52
 
3.5%
ValueCountFrequency (%)
9 52
 
3.5%
8 49
 
3.3%
7 74
 
5.0%
6 70
 
4.8%
5 63
 
4.3%
4 139
 
9.5%
3 159
 
10.8%
2 146
 
9.9%
1 521
35.4%
0 197
 
13.4%

Hora extra
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Não
1054 
Sim
416 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSim
2nd rowNão
3rd rowSim
4th rowSim
5th rowNão

Common Values

ValueCountFrequency (%)
Não 1054
71.7%
Sim 416
 
28.3%

Length

2025-04-15T19:02:36.092479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:36.418865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
não 1054
71.7%
sim 416
 
28.3%

Most occurring characters

ValueCountFrequency (%)
N 1054
23.9%
ã 1054
23.9%
o 1054
23.9%
S 416
 
9.4%
i 416
 
9.4%
m 416
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1054
23.9%
ã 1054
23.9%
o 1054
23.9%
S 416
 
9.4%
i 416
 
9.4%
m 416
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1054
23.9%
ã 1054
23.9%
o 1054
23.9%
S 416
 
9.4%
i 416
 
9.4%
m 416
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1054
23.9%
ã 1054
23.9%
o 1054
23.9%
S 416
 
9.4%
i 416
 
9.4%
m 416
 
9.4%

% aumento sal
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.209524
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-04-15T19:02:36.722487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6599377
Coefficient of variation (CV)0.2406346
Kurtosis-0.30059822
Mean15.209524
Median Absolute Deviation (MAD)2
Skewness0.82112798
Sum22358
Variance13.395144
MonotonicityNot monotonic
2025-04-15T19:02:37.061986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11 210
14.3%
13 209
14.2%
14 201
13.7%
12 198
13.5%
15 101
6.9%
18 89
6.1%
17 82
 
5.6%
16 78
 
5.3%
19 76
 
5.2%
22 56
 
3.8%
Other values (5) 170
11.6%
ValueCountFrequency (%)
11 210
14.3%
12 198
13.5%
13 209
14.2%
14 201
13.7%
15 101
6.9%
16 78
 
5.3%
17 82
 
5.6%
18 89
6.1%
19 76
 
5.2%
20 55
 
3.7%
ValueCountFrequency (%)
25 18
 
1.2%
24 21
 
1.4%
23 28
 
1.9%
22 56
3.8%
21 48
3.3%
20 55
3.7%
19 76
5.2%
18 89
6.1%
17 82
5.6%
16 78
5.3%

Avaliação desemp
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
1244 
4
226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Length

2025-04-15T19:02:37.472293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:37.817002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring characters

ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Satisf relac
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
459 
4
432 
2
303 
1
276 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Length

2025-04-15T19:02:38.250799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:38.642549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring characters

ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Opc ações
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
631 
1
596 
2
158 
3
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Length

2025-04-15T19:02:39.090704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:39.736876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Anos traba
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.279592
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-04-15T19:02:40.183647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7807817
Coefficient of variation (CV)0.68981057
Kurtosis0.91826954
Mean11.279592
Median Absolute Deviation (MAD)4
Skewness1.1171719
Sum16581
Variance60.540563
MonotonicityNot monotonic
2025-04-15T19:02:40.691237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 202
 
13.7%
6 125
 
8.5%
8 103
 
7.0%
9 96
 
6.5%
5 88
 
6.0%
7 81
 
5.5%
1 81
 
5.5%
4 63
 
4.3%
12 48
 
3.3%
3 42
 
2.9%
Other values (30) 541
36.8%
ValueCountFrequency (%)
0 11
 
0.7%
1 81
5.5%
2 31
 
2.1%
3 42
 
2.9%
4 63
4.3%
5 88
6.0%
6 125
8.5%
7 81
5.5%
8 103
7.0%
9 96
6.5%
ValueCountFrequency (%)
40 2
 
0.1%
38 1
 
0.1%
37 4
0.3%
36 6
0.4%
35 3
 
0.2%
34 5
0.3%
33 7
0.5%
32 9
0.6%
31 9
0.6%
30 7
0.5%

Treinam ultm ano
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7993197
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-04-15T19:02:41.109177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2892706
Coefficient of variation (CV)0.46056569
Kurtosis0.49499299
Mean2.7993197
Median Absolute Deviation (MAD)1
Skewness0.55312417
Sum4115
Variance1.6622187
MonotonicityNot monotonic
2025-04-15T19:02:41.508117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 547
37.2%
3 491
33.4%
4 123
 
8.4%
5 119
 
8.1%
1 71
 
4.8%
6 65
 
4.4%
0 54
 
3.7%
ValueCountFrequency (%)
0 54
 
3.7%
1 71
 
4.8%
2 547
37.2%
3 491
33.4%
4 123
 
8.4%
5 119
 
8.1%
6 65
 
4.4%
ValueCountFrequency (%)
6 65
 
4.4%
5 119
 
8.1%
4 123
 
8.4%
3 491
33.4%
2 547
37.2%
1 71
 
4.8%
0 54
 
3.7%

Equil vida-trab
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
893 
2
344 
4
153 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Length

2025-04-15T19:02:41.937738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T19:02:42.330440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring characters

ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Anos empresa
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0081633
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-04-15T19:02:42.766165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1265252
Coefficient of variation (CV)0.87419841
Kurtosis3.9355088
Mean7.0081633
Median Absolute Deviation (MAD)3
Skewness1.7645295
Sum10302
Variance37.53431
MonotonicityNot monotonic
2025-04-15T19:02:43.278771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5 196
13.3%
1 171
11.6%
3 128
8.7%
2 127
8.6%
10 120
8.2%
4 110
 
7.5%
7 90
 
6.1%
9 82
 
5.6%
8 80
 
5.4%
6 76
 
5.2%
Other values (27) 290
19.7%
ValueCountFrequency (%)
0 44
 
3.0%
1 171
11.6%
2 127
8.6%
3 128
8.7%
4 110
7.5%
5 196
13.3%
6 76
 
5.2%
7 90
6.1%
8 80
5.4%
9 82
5.6%
ValueCountFrequency (%)
40 1
 
0.1%
37 1
 
0.1%
36 2
 
0.1%
34 1
 
0.1%
33 5
0.3%
32 3
0.2%
31 3
0.2%
30 1
 
0.1%
29 2
 
0.1%
27 2
 
0.1%

Anos cargo atual
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2292517
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-04-15T19:02:43.723557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.623137
Coefficient of variation (CV)0.85668513
Kurtosis0.47742077
Mean4.2292517
Median Absolute Deviation (MAD)3
Skewness0.91736316
Sum6217
Variance13.127122
MonotonicityNot monotonic
2025-04-15T19:02:44.140218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 372
25.3%
0 244
16.6%
7 222
15.1%
3 135
 
9.2%
4 104
 
7.1%
8 89
 
6.1%
9 67
 
4.6%
1 57
 
3.9%
6 37
 
2.5%
5 36
 
2.4%
Other values (9) 107
 
7.3%
ValueCountFrequency (%)
0 244
16.6%
1 57
 
3.9%
2 372
25.3%
3 135
 
9.2%
4 104
 
7.1%
5 36
 
2.4%
6 37
 
2.5%
7 222
15.1%
8 89
 
6.1%
9 67
 
4.6%
ValueCountFrequency (%)
18 2
 
0.1%
17 4
 
0.3%
16 7
 
0.5%
15 8
 
0.5%
14 11
 
0.7%
13 14
 
1.0%
12 10
 
0.7%
11 22
 
1.5%
10 29
2.0%
9 67
4.6%

Anos ult prom
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1877551
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-04-15T19:02:44.529303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2224303
Coefficient of variation (CV)1.4729392
Kurtosis3.6126731
Mean2.1877551
Median Absolute Deviation (MAD)1
Skewness1.98429
Sum3216
Variance10.384057
MonotonicityNot monotonic
2025-04-15T19:02:44.904308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 581
39.5%
1 357
24.3%
2 159
 
10.8%
7 76
 
5.2%
4 61
 
4.1%
3 52
 
3.5%
5 45
 
3.1%
6 32
 
2.2%
11 24
 
1.6%
8 18
 
1.2%
Other values (6) 65
 
4.4%
ValueCountFrequency (%)
0 581
39.5%
1 357
24.3%
2 159
 
10.8%
3 52
 
3.5%
4 61
 
4.1%
5 45
 
3.1%
6 32
 
2.2%
7 76
 
5.2%
8 18
 
1.2%
9 17
 
1.2%
ValueCountFrequency (%)
15 13
 
0.9%
14 9
 
0.6%
13 10
 
0.7%
12 10
 
0.7%
11 24
 
1.6%
10 6
 
0.4%
9 17
 
1.2%
8 18
 
1.2%
7 76
5.2%
6 32
2.2%

Anos gerente atual
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1231293
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-04-15T19:02:45.272267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5681361
Coefficient of variation (CV)0.86539517
Kurtosis0.17105808
Mean4.1231293
Median Absolute Deviation (MAD)3
Skewness0.83345099
Sum6061
Variance12.731595
MonotonicityNot monotonic
2025-04-15T19:02:45.646083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 344
23.4%
0 263
17.9%
7 216
14.7%
3 142
9.7%
8 107
 
7.3%
4 98
 
6.7%
1 76
 
5.2%
9 64
 
4.4%
5 31
 
2.1%
6 29
 
2.0%
Other values (8) 100
 
6.8%
ValueCountFrequency (%)
0 263
17.9%
1 76
 
5.2%
2 344
23.4%
3 142
9.7%
4 98
 
6.7%
5 31
 
2.1%
6 29
 
2.0%
7 216
14.7%
8 107
 
7.3%
9 64
 
4.4%
ValueCountFrequency (%)
17 7
 
0.5%
16 2
 
0.1%
15 5
 
0.3%
14 5
 
0.3%
13 14
 
1.0%
12 18
 
1.2%
11 22
 
1.5%
10 27
 
1.8%
9 64
4.4%
8 107
7.3%

Interactions

2025-04-15T19:02:12.839279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:13.648657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:18.355531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:23.114518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:27.858311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:32.459224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:36.939112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:41.399884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:45.550004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:49.926800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:54.542886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:59.111984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:03.700976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:08.219744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:13.124988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:14.051365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:18.645798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:23.403832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:28.143581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:32.737952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:37.214515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:41.668621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:45.837618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:50.225757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:54.820972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:59.421155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:03.999753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:08.503189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:13.458291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:14.418981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:18.951922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:23.730823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:28.461004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:33.067442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:37.520215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:41.966682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:46.147671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:50.575610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:55.128765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:59.757022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:04.327632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:08.823970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:13.770112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:14.753908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:19.241346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:24.024228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:28.750930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:33.351183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:37.797885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:42.241214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:46.429780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:50.877241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:55.633512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:00.057087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:04.628141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:09.112503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:14.143819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:15.141224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:19.569023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:24.359955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:29.073331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:33.685351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:38.123616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:42.548268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:46.750389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:51.227233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:55.954796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:00.382969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:04.966423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:09.446734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:14.509789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:15.527626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:19.931746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:24.732361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:29.404890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:34.008807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:38.401823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:42.859993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:47.094347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:51.554314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:56.274343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:00.727613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:05.306907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:10.015812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:14.824941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:15.860210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:20.262212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:25.042538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:29.713298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:34.309557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:38.675119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:43.135162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:47.379829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:51.900282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:56.560876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:01.019864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:05.603532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:10.308035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:15.133678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:16.178701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:20.577167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:25.342919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:30.012858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:34.604342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:38.957764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:43.396778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:47.667460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:52.197165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:56.840302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:01.309632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:05.899034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:10.590376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:15.463918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:16.504182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:20.920328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:25.670231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:30.332747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:34.928975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:39.259065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:43.687408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:47.978335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:52.516238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:57.211328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:01.681548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:06.220641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:10.910786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:15.824777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:16.823502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:21.321697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:26.016391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:30.668589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:35.269336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:39.581177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:44.008197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:48.306281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:52.845399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:57.540428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:02.024903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:06.562747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:11.237681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:16.147205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:17.117007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:21.665869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:26.553489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:30.998771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:35.591135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:39.887118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:44.301565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:48.619440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:53.156654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:57.820657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:02.351501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:06.887539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:11.547159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:16.551644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:17.431880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:22.033863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:26.894250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:31.377852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:35.930864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:40.212294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:44.622181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:48.944032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:53.500759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:58.151661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:02.688118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:07.226806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:11.871974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:16.903627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:17.741862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:22.399453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:27.226178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:31.747745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:36.269378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:40.753043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:44.934783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:49.273051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:53.882741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:58.468737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:03.022927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:07.555907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:12.198166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:17.256837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:18.045177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:22.752413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:27.539846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:32.111678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:36.600309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:41.073165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:45.238418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:49.590059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:54.204608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:01:58.791810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:03.361090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:07.880453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T19:02:12.507859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-04-15T19:02:46.044394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
% aumento salAnos cargo atualAnos empresaAnos gerente atualAnos trabaAnos ult promAttritionAvaliação desempCargoDepartamentoDistância casaEnvolv trabEquil vida-trabEstado civilFormação acadGêneroHora extraIdadeNº empresas trabNível cargoOpc açõesRenda mensalSatisf ambSatisf relacSatisf trabTarifa diáriaTarifa horaTarifa mensalTreinam ultm anoViagens trabÁrea form
% aumento sal1.000-0.026-0.054-0.026-0.026-0.0550.0000.9970.0000.0000.0300.0360.0000.0000.0210.0490.0000.0080.0000.0000.000-0.0340.0000.0270.0000.025-0.010-0.005-0.0040.0300.000
Anos cargo atual-0.0261.0000.8540.7250.4930.5060.1690.0310.1320.0000.0140.0000.0250.0400.0290.0790.0420.198-0.1280.2410.0230.3950.0360.0000.0000.007-0.034-0.0070.0050.0000.000
Anos empresa-0.0540.8541.0000.8430.5940.5200.1730.0000.1880.0000.0110.0530.0200.0000.0710.0660.0180.252-0.1710.3530.0120.4640.0310.0000.000-0.010-0.029-0.0300.0010.0000.000
Anos gerente atual-0.0260.7250.8431.0000.4950.4670.1790.0300.1180.0000.0040.0440.0310.0000.0000.0000.0000.195-0.1440.2320.0300.3650.0000.0000.000-0.005-0.014-0.035-0.0120.0640.000
Anos traba-0.0260.4930.5940.4951.0000.3350.2080.0000.2930.024-0.0030.0000.0000.0690.0950.0000.0000.6570.3150.5390.0640.7100.0000.0310.0240.021-0.0120.013-0.0140.0000.030
Anos ult prom-0.0550.5060.5200.4670.3351.0000.0270.0000.1110.000-0.0050.0000.0000.0350.0000.0000.0110.174-0.0670.2060.0560.2650.0000.0500.000-0.038-0.052-0.0160.0100.0300.000
Attrition0.0000.1690.1730.1790.2080.0271.0000.0000.2310.0770.0680.1320.0950.1730.0000.0090.2430.2130.1070.2160.1980.2170.1150.0390.0990.0620.0440.0100.0790.1230.087
Avaliação desemp0.9970.0310.0000.0300.0000.0000.0001.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0150.0000.0000.000
Cargo0.0000.1320.1880.1180.2930.1110.2310.0001.0000.9370.0000.0000.0290.0610.0510.0740.0000.1750.0790.5690.0390.4230.0000.0300.0000.0000.0230.0000.0000.0000.336
Departamento0.0000.0000.0000.0000.0240.0000.0770.0000.9371.0000.0000.0000.0470.0300.0000.0260.0000.0000.0320.2120.0000.1870.0180.0200.0290.0000.0000.0000.0000.0000.588
Distância casa0.0300.0140.0110.004-0.003-0.0050.0680.0550.0000.0001.0000.0340.0000.0000.0000.0260.064-0.019-0.0100.0540.0000.0030.0000.0190.000-0.0030.0200.040-0.0250.0250.000
Envolv trab0.0360.0000.0530.0440.0000.0000.1320.0000.0000.0000.0341.0000.0000.0240.0000.0000.0000.0250.0000.0000.0220.0460.0340.0000.0000.0160.0000.0000.0130.0160.000
Equil vida-trab0.0000.0250.0200.0310.0000.0000.0950.0000.0290.0470.0000.0001.0000.0000.0000.0000.0000.0330.0510.0000.0190.0000.0000.0000.0000.0120.0000.0340.0000.0000.027
Estado civil0.0000.0400.0000.0000.0690.0350.1730.0000.0610.0300.0000.0240.0001.0000.0000.0320.0000.1410.0380.0460.5810.0610.0190.0250.0000.0850.0000.0000.0000.0350.000
Formação acad0.0210.0290.0710.0000.0950.0000.0000.0000.0510.0000.0000.0000.0000.0001.0000.0000.0010.1530.1010.0880.0270.0940.0190.0160.0150.0170.0000.0370.0270.0000.055
Gênero0.0490.0790.0660.0000.0000.0000.0090.0000.0740.0260.0260.0000.0000.0320.0001.0000.0310.0000.0000.0480.0000.0460.0000.0000.0000.0310.0000.0000.0000.0370.000
Hora extra0.0000.0420.0180.0000.0000.0110.2430.0000.0000.0000.0640.0000.0000.0000.0010.0311.0000.0000.0000.0000.0000.0000.0600.0250.0220.0000.0640.0000.0990.0240.000
Idade0.0080.1980.2520.1950.6570.1740.2130.0000.1750.000-0.0190.0250.0330.1410.1530.0000.0001.0000.3530.2950.0930.4720.0060.0350.0000.0070.0290.0170.0000.0410.000
Nº empresas trab0.000-0.128-0.171-0.1440.315-0.0670.1070.0000.0790.032-0.0100.0000.0510.0380.1010.0000.0000.3531.0000.1130.0000.1900.0000.0000.0000.0370.0190.020-0.0470.0000.060
Nível cargo0.0000.2410.3530.2320.5390.2060.2160.0000.5690.2120.0540.0000.0000.0460.0880.0480.0000.2950.1131.0000.0690.8640.0000.0000.0000.0000.0000.0160.0170.0000.091
Opc ações0.0000.0230.0120.0300.0640.0560.1980.0000.0390.0000.0000.0220.0190.5810.0270.0000.0000.0930.0000.0691.0000.0560.0000.0300.0000.0400.0520.0000.0000.0000.032
Renda mensal-0.0340.3950.4640.3650.7100.2650.2170.0000.4230.1870.0030.0460.0000.0610.0940.0460.0000.4720.1900.8640.0561.0000.0000.0430.0000.016-0.0200.054-0.0350.0250.073
Satisf amb0.0000.0360.0310.0000.0000.0000.1150.0000.0000.0180.0000.0340.0000.0190.0190.0000.0600.0060.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.031
Satisf relac0.0270.0000.0000.0000.0310.0500.0390.0000.0300.0200.0190.0000.0000.0250.0160.0000.0250.0350.0000.0000.0300.0430.0001.0000.0000.0000.0000.0550.0000.0000.040
Satisf trab0.0000.0000.0000.0000.0240.0000.0990.0260.0000.0290.0000.0000.0000.0000.0150.0000.0220.0000.0000.0000.0000.0000.0000.0001.0000.0000.0100.0480.0210.0000.017
Tarifa diária0.0250.007-0.010-0.0050.021-0.0380.0620.0000.0000.000-0.0030.0160.0120.0850.0170.0310.0000.0070.0370.0000.0400.0160.0000.0000.0001.0000.024-0.032-0.0110.0290.039
Tarifa hora-0.010-0.034-0.029-0.014-0.012-0.0520.0440.0000.0230.0000.0200.0000.0000.0000.0000.0000.0640.0290.0190.0000.052-0.0200.0000.0000.0100.0241.000-0.0150.0000.0000.031
Tarifa mensal-0.005-0.007-0.030-0.0350.013-0.0160.0100.0150.0000.0000.0400.0000.0340.0000.0370.0000.0000.0170.0200.0160.0000.0540.0000.0550.048-0.032-0.0151.000-0.0100.0000.000
Treinam ultm ano-0.0040.0050.001-0.012-0.0140.0100.0790.0000.0000.000-0.0250.0130.0000.0000.0270.0000.0990.000-0.0470.0170.000-0.0350.0000.0000.021-0.0110.000-0.0101.0000.0000.044
Viagens trab0.0300.0000.0000.0640.0000.0300.1230.0000.0000.0000.0250.0160.0000.0350.0000.0370.0240.0410.0000.0000.0000.0250.0000.0000.0000.0290.0000.0000.0001.0000.000
Área form0.0000.0000.0000.0000.0300.0000.0870.0000.3360.5880.0000.0000.0270.0000.0550.0000.0000.0000.0600.0910.0320.0730.0310.0400.0170.0390.0310.0000.0440.0001.000

Missing values

2025-04-15T19:02:17.829711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-15T19:02:19.265752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IdadeAttritionViagens trabTarifa diáriaDepartamentoDistância casaFormação acadÁrea formSatisf ambGêneroTarifa horaEnvolv trabNível cargoCargoSatisf trabEstado civilRenda mensalTarifa mensalNº empresas trabHora extra% aumento salAvaliação desempSatisf relacOpc açõesAnos trabaTreinam ultm anoEquil vida-trabAnos empresaAnos cargo atualAnos ult promAnos gerente atual
041SimRaramente1102Vendas1.612Ciências biológicas2Feminino9432Executivo de vendas4Solteiro5993194798Sim113108016405
149NãoFrequentemente279Pesquisa e Desenvolvimento12.871Ciências biológicas3Masculino6122Cientista pesquisador2Casado5130249071Não23441103310717
237SimRaramente1373Pesquisa e Desenvolvimento3.222Outros4Masculino9221Técnico de laboratório3Solteiro209023966Sim153207330000
333NãoFrequentemente1392Pesquisa e Desenvolvimento4.834Ciências biológicas4Feminino5631Cientista pesquisador3Casado2909231591Sim113308338730
427NãoRaramente591Pesquisa e Desenvolvimento3.221Ciências médicas1Masculino4031Técnico de laboratório2Casado3468166329Não123416332222
532NãoFrequentemente1005Pesquisa e Desenvolvimento3.222Ciências biológicas4Masculino7931Técnico de laboratório4Solteiro3068118640Não133308227736
659NãoRaramente1324Pesquisa e Desenvolvimento4.833Ciências médicas3Feminino8141Técnico de laboratório1Casado267099644Sim2041312321000
730NãoRaramente1358Pesquisa e Desenvolvimento38.621Ciências biológicas4Masculino6731Técnico de laboratório3Divorciado2693133351Não224211231000
838NãoFrequentemente216Pesquisa e Desenvolvimento37.013Ciências biológicas4Masculino4423Diretor de produção3Solteiro952687870Não2142010239718
936NãoRaramente1299Pesquisa e Desenvolvimento43.453Ciências médicas3Masculino9432Representante de saúde3Casado5237165776Não1332217327777
IdadeAttritionViagens trabTarifa diáriaDepartamentoDistância casaFormação acadÁrea formSatisf ambGêneroTarifa horaEnvolv trabNível cargoCargoSatisf trabEstado civilRenda mensalTarifa mensalNº empresas trabHora extra% aumento salAvaliação desempSatisf relacOpc açõesAnos trabaTreinam ultm anoEquil vida-trabAnos empresaAnos cargo atualAnos ult promAnos gerente atual
146029NãoRaramente468Pesquisa e Desenvolvimento45.064Ciências médicas4Feminino7321Cientista pesquisador1Solteiro378584891Não143205315404
146150SimRaramente410Vendas45.063Marketing4Masculino3923Executivo de vendas1Divorciado10854165864Sim1332120333220
146239NãoRaramente722Vendas38.621Marketing2Feminino6024Executivo de vendas4Casado1203188280Não11311212220996
146331NãoNão viaja325Pesquisa e Desenvolvimento8.053Ciências médicas2Masculino7432Diretor de produção1Solteiro993637870Não1932010239417
146426NãoRaramente1167Vendas8.053Outros4Feminino3021Representante de vendas3Solteiro2966213780Não183405234200
146536NãoFrequentemente884Pesquisa e Desenvolvimento37.012Ciências médicas3Masculino4142Técnico de laboratório4Casado2571122904Não1733117335203
146639NãoRaramente613Pesquisa e Desenvolvimento9.661Ciências médicas4Masculino4223Representante de saúde1Casado9991214574Não153119537717
146727NãoRaramente155Pesquisa e Desenvolvimento6.443Ciências biológicas2Masculino8742Diretor de produção2Casado614251741Sim204216036203
146849NãoFrequentemente1023Vendas3.223Ciências médicas4Masculino6322Executivo de vendas2Casado5390132432Não1434017329608
146934NãoRaramente628Pesquisa e Desenvolvimento12.873Ciências médicas2Masculino8242Técnico de laboratório3Casado4404102282Não123106344312